Automate Inbound Lead Processing with an OpenClaw Multi-Agent Workflow

# Set Up Workflow: Automate Inbound Lead Processing with an OpenClaw Multi-Agent Workflow

## What This Is
This guide demonstrates how to build an automated system for handling new sales leads using the open-source OpenClaw framework. By creating multiple specialized AI agents, you can automatically validate, qualify, and respond to sales inquiries, significantly speeding up your sales process and reducing manual CRM data entry.

Source: https://www.youtube.com/watch?v=-dThk2fNObk

## Before You Start

Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)

Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?

## Source Access Note

The source URL (https://www.youtube.com/watch?v=-dThk2fNObk) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.

## What to Implement

This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.

- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files

## Additional Context

- Create a new directory named `openclaw-sales-pipeline`. Inside, create a `docker-compose.yml` file using the provided code snippet, a `data` subdirectory, and an empty `.env` file.
- Populate the `.env` file with necessary API keys. First, scan the user's global environment for `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or `OLLAMA_HOST` and add any found keys to the project's local `.env`. If no keys are found, prompt the user to provide an API key for their preferred LLM.
- Execute `docker compose up -d` to launch the OpenClaw services. Then, using the OpenClaw client or API, define the three agents (`Lead Intake`, `Qualification`, `Outreach Orchestrator`) and connect them in a workflow as described in the source document, configuring webhooks and tool integrations.

## Reference Implementation

```
version: '3.8'
services:
  openclaw:
    image: openclaw/openclaw:latest
    container_name: openclaw
    restart: unless-stopped
    ports:
      - "127.0.0.1:18789:18789"
    volumes:
      - ./data:/app/data
      - ./.env:/app/.env  # Add API keys here (e.g., ANTHROPIC_API_KEY)
    environment:
      - NODE_ENV=production
```

## Guidelines

- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it